Reverse Logistics of Waste Plastic Model Optimization Using Genetic Algorithm

International Journal of Civil Engineering

© 2024 by SSRG - IJCE Journal

Volume 11 Issue 4

Year of Publication : 2024

Authors : Sachin Kumar, Sanjeev Sinha

10.14445/23488352/IJCE-V11I4P102

How to Cite?

Sachin Kumar, Sanjeev Sinha, "Reverse Logistics of Waste Plastic Model Optimization Using Genetic Algorithm," SSRG International Journal of Civil Engineering, vol. 11,  no. 4, pp. 8-16, 2024. Crossref, https://doi.org/10.14445/23488352/IJCE-V11I4P102

Abstract:

Plastic waste management is critical on a global scale because of the widespread environmental and economic consequences. Businesses are confronted with increasing cost challenges, forcing an increasing number of them to investigate alternatives for more cost-effective product returns and recycling solutions. Several studies have investigated the challenges of reverse logistics for various waste materials such as C&D wastes, iron, and aluminium; there exists a significant gap in research addressing the holistic incorporation of reverse logistics for plastic waste. This research aims to fill this gap by proposing a comprehensive model that not only minimizes the total reverse logistics cost but also determines the optimal number of recycling plants. The proposed waste plastic recycling model introduces a distinctive two-stage modelling approach, integrating collection centres before recycling plants. This novelty is crucial for minimizing transportation costs from municipalities to recycling plants, given that plastic is a low-density material and is often mixed with non-plastic substances like dirt, iron, and aluminium. To address this issue effectively, the proposed model employs a mixed-integer linear programming deterministic model, solved using the Genetic Algorithm (GA). The model’s effectiveness is validated through practical applications involving illustrative examples that demonstrate its applicability to the complexities of reverse logistics in the waste plastic management field.

Keywords:

Plastic recycling, Reverse logistics, Waste management, Genetic algorithm, Mixed integer linear programming.

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